Assessing foresight to advance management of complex global problems
Notice bibliographique
Résumé
Many people do not like thinking about the future. If they do, over 50% of Canadians think “our way of life” (p. 7) will end within 100 years and over 80% of Canadians think “we need to change our worldview and way of life if we are to create a better future for the world” (Randle & Eckersley, 2015, p. 9). There is a good reason for this. Alarms have sounded over global urgent complex problems with potential for catastrophic consequences such as the development of artificial intelligence, climate change, mass extinction, nuclear war and pandemics (Marien & Halal, 2011). Society is also increasingly fragmenting as imminent crises build on lack of understanding, the sense of incapacity to act, fear, distrust, blame and a lack of hope. This struggle for humanity’s survival is complicated by the turbulent global environment in which institutions continue to follow path-dependent trajectories set forth in a different time and context. Governments at various levels face a problem of “fit” between current structures and processes, that have not progressed sufficiently to meet changing needs of a global society mired in complexity and governance challenges. However, hope exists. Incremental progress on many fronts and a massive amount of efforts and resources are being engaged worldwide. There are emerging fields, lenses and tools that can potentially alleviate complex problems and address this emergency. The purpose of this dissertation is to understand and assess dialogue-based foresight practices being applied towards complex problems in Canada to provide insights into how these practices can assist society to alleviate global urgent complex problems and their impacts, within this backdrop of looming crises. Foresight, alternatively known as future studies or scenario-building, is a forward-looking practice recognized and used globally with over 100 research organizations focused on foresight, widespread usage by firms and over 18 countries involved in foresight activities (Berze, 2014b). Overall literature findings suggest foresight is widely and at least incrementally effective with a number of impacts in various areas (Calof, Miller, & Jackson, 2012; March, Therond, & Leenhardt, 2012; Meissner, Gokhberg, & Sokolov, 2013) but the extent of this effectiveness, the mechanisms involved, and the specific foresight benefits per type of project needs further research and evidence. For instance, limited literature exists on whether foresight can transform complex situations and if so, under what conditions. Thus, opportunities exist for assessing and increasing foresight’s impact. This dissertation is a contextualized, systematic empirical study that taps into transdisciplinary literature and practice, case studies of how foresight has been used to address specific types of complex problems in Canada, as well as surveys and interviews with foresight experts and participants. This dissertation uses a foresight community scan and a comparative case study approach to provide practical and theoretical benefits to foresight and complex problem area stakeholders. The research focuses on studying the broad interactions of foresight and identifying the impacts of dialogue-based foresight projects on people and the outcomes of complex problems. The dissertation concludes that dialogue-based foresight is a valuable and unique practice for ameliorating complex problems and their consequences. Insights are offered towards dialogue-based foresight’s potential contributions within the context of other efforts directed at humanity’s struggle for survival and global complex problems. These insights can then foster the further development and application of dialogue-based foresight on a global scale to alleviate complex problems and their effects. The dissertation outlines recommendations on key next steps to realize these potential contributions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,029 | 0,075 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,011 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,013 | 0,020 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».